Classification System of Racial Type and Anti-Aging Detection Based on T-Zone and U-Zone Features in Facial Images Using Convolutional Neural Networks and Haar Cascade Methods
Classification System of Racial Type and Anti-Aging Detection Based on T-Zone and U-Zone Features in Facial Images Using Convolutional Neural Networks and Haar Cascade Methods |
||
 |
 |
|
| © 2025 by IJETT Journal | ||
| Volume-73 Issue-7 |
||
| Year of Publication : 2025 | ||
| Author : Indriyani, Paula Dewanti, Made Sudarma | ||
| DOI : 10.14445/22315381/IJETT-V73I7P117 | ||
How to Cite?
Indriyani, Paula Dewanti, Made Sudarma, "Classification System of Racial Type and Anti-Aging Detection Based on T-Zone and U-Zone Features in Facial Images Using Convolutional Neural Networks and Haar Cascade Methods," International Journal of Engineering Trends and Technology, vol. 73, no. 7, pp.217-233, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I7P117
Abstract
This research presents a novel computational model for racial identification and anti-aging skin type analysis, built upon a hybrid architecture that integrates the Haar Cascade and a Convolutional Neural Network (CNN) method. This Model addresses the limited application of objective skin metrics in conventional racial classification by focusing on the analysis of unique features within digital imagery of the facial T-zone and U-zone. Fine-tuned over 120 training epochs with the Adam optimizer, the system achieved a classification accuracy of 99.35% for race and 93.91% for skin type. This high degree of Precision underscores the efficacy of leveraging specific facial zones for developing robust and highly accurate classification systems, confirming that such computational approaches can overcome the limitations of traditional methods.
Keywords
Convolutional Neural Network, Haar cascade, Anti-aging, Race, Zone T, Zone U, Facial skin.
References
[1] Rahmadina, and Husnarika Febriani, “Cell Biology The Smallest Unit of a Living Body,” 1st ed., 2017.
[Publisher Link]
[2] Made Nopen Supriadi, “A Theological Evaluation of Racism,” Manna Rafflesia, vol. 4, no. 1, pp. 75-91, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Sri Handayani, Anatomy and Physiology of the Human Body, Republic of Indonesia Ministry of Law and Human Rights, 2014. [Online]. Available: http://repository.stikes-yogyakarta.ac.id/id/eprint/24/2/HKI%20Anatomi%20Fisiologi%20Tubuh%20Manusia.pdf
[4] Adrian Sergiu Darabant, Diana Borza, and Radu Danescu, “Recognizing Human Races Through Machine Learning-A Multi-Network, Multi-Features Study,” Mathematics, vol. 9, no. 2, pp. 1-19, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Chiun-Li Chin et al., “Facial Skin Image Classification System Using Convolutional Neural Networks Deep Learning Algorithm,” 2018 9th International Conference on Awareness Science and Technology (iCAST), Fukuoka, Japan, pp. 51-55, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Jhan S. Alarifi et al., “Facial Skin Classification Using Convolutional Neural Networks,” International Conference on Image Analysis and Recognition, Montreal, QC, Canada, pp. 479-485, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Shlomi Talala et al., “Emotion Classification Based on Pulsatile Images Extracted from Short Facial Videos via Deep Learning,” Sensors, vol. 24, no. 8, pp. 1-16, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[8] David Belcar, Petra Grd, and Igor Tomičić, “Automatic Ethnicity Classification from Middle Part of the Face Using Convolutional Neural Networks,” Informatics, vol. 9, no. 1, pp. 1-25, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Harleen Kaur, and Arisha Mirza, “Face Detection Using Haar Cascades Classifier,” Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, Jamia Hamdard, New Delhi, India, pp. 1-7, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Elena V. Stepanova, and Michael J Strube, “The Role of Skin Color and Facial Physiognomy in Racial Categorization: Moderation by Implicit Racial Attitudes,” Journal of Experimental Social Psychology, vol. 48, no. 4, pp. 867-878, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Emily B. Crosby et al., “Neurobehavioral Impairments Caused by Developmental Imidacloprid Exposure in Zebrafish,” Neurotoxicology and Teratology, vol. 49, pp. 81-90, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Abdulwahid Al Abdulwahid, “Classification of Ethnicity Using Efficient CNN Models on MORPH and FERET Datasets Based on Face Biometrics,” Applied Sciences, vol. 13, no. 12, pp. 1-29, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Neira Puizina-Ivić, “Skin Aging,” Acta Dermatoven, vol. 17, no. 2, pp. 47-54, 2008.
[Google Scholar] [Publisher Link]
[14] Abdul Kader Mohiuddin, “Skin Aging & Modern Age Anti-aging Strategies,” International Journal of Clinical Dermatology & Research (IJCDR), vol. 7, no. 4, pp. 209-240, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Raghav Agarwal, and Deepthi Godavarthi, “Skin Disease Classification Using CNN Algorithms,” EAI Endorsed Trans Perv Health Technology, vol. 9, no. 1, pp. 1-8, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Shervin Minaee et al., “Image Segmentation Using Deep Learning: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3523-3542, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Licheng Jiao et al., “A Survey of Deep Learning-Based Object Detection,” IEEE Access, vol. 7, pp. 128837-128868, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Andreas Kamilaris, and Francesc X. Prenafeta-Boldú, “Deep Learning in Agriculture: A Survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70-90, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Mark Sandler et al., “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 4510-4520, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Kaiming He et al., “Momentum Contrast for Unsupervised Visual Representation Learning,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, pp. 9726-9735, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Khalil Khan et al., “A Facial Feature Discovery Framework for Race Classification Using Deep Learning,” arXiv Preprint, pp. 1-14, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Yang Zhong, Josephine Sullivan, and Haibo Li, “Face Attribute Prediction Using off-the-Shelf CNN Features,” 2016 International Conference on Biometrics (ICB), Halmstad, pp. 1-7, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Li Cuimei et al., “Human Face Detection Algorithm via Haar Cascade Classifier Combined with Three Additional Classifiers,” 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), Yangzhou, China, pp. 483-487, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[24] A. Singh, H. Herunde, and F. Furtado, “Modified Haar-Cascade Model for Face Detection Issues,” International Journal of Research in Industrial Engineering, vol. 9, no. 2, pp. 143-171, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[25] S.L. Phung, A. Bouzerdoum, and D. Chai, “Skin Segmentation Using Color Pixel Classification: Analysis and Comparison,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 1, pp. 148-154, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Adekanmi Adeyinka Adegun, Serestina Viriri, and Muhammad Haroon Yousaf, “A Probabilistic-Based Deep Learning Model for Skin Lesion Segmentation,” Applied Sciences, vol. 11, no. 7, pp. 1-13, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Wei Ren Tan et al., “A Fusion Approach for Efficient Human Skin Detection,” IEEE Transactions on Industrial Informatics, vol. 8, no. 1, pp. 138-147, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[28] A. Bulat, and G. Tzimiropoulos, “How Far are We from Solving the 2D & 3D Face Alignment Problem? (and a Dataset of 230,000 3D Facial Landmarks),” 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, pp. 1021-1030, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Rajeev Ranjan et al., “An All-In-One Convolutional Neural Network for Face Analysis,” 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), Washington, DC, USA, pp. 17-24, 2017.
[CrossRef] [Google Scholar] [Publisher Link]